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Record W4311735866 · doi:10.1093/ofid/ofac492.350

272. Do Empiric Antibiotics Improve Outcomes in Clinically Stable Patients Admitted with COVID-19 Pneumonia? Retrospective Cohort Study of 221 U.S. Hospitals, March 1st, 2020-December 31st, 2020

2022· article· en· W4311735866 on OpenAlexaff
Alexander Lawandi, Jeffrey R. Strich, Xioabai Li, Christina Yek, Sarah Warner, Sameer S. Kadri

Bibliographic record

VenueOpen Forum Infectious Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicinePneumoniaMechanical ventilationPneumonia severity indexRetrospective cohort studyEmergency medicinePropensity score matchingInternal medicineCommunity-acquired pneumoniaPediatricsIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Background Patients admitted with COVID19 pneumonia often receive initial empiric antibacterial therapy (IEAT) despite a known low probability of bacterial co-infection. However, evidence supporting this practice is lacking. We studied the impact of IEAT on the risk of in-hospital mortality, clinical deterioration and antibiotic-associated risks in stable inpatients with COVID-19. Methods Adult inpatients coded for COVID-19 pneumonia stable (no mechanical ventilation or vasopressors) on admission (+1 day) without a clear indication for antibiotics, were identified at hospitals in the Premier Healthcare Database. Patients who received IEAT, defined as the receipt of ≥ 1 antibacterial agent on admission (+1 day), were compared to a control group, using binomial regression with overlap weight matching and downstream adjustment for baseline characteristics (age, gender, race, admission month, surge index, Elixhauser score, any AOFS organ failure POA, ICU admission on day 0 to +2, receipt of remdesivir, corticosteroids, and tocilizumab). The primary outcome was in-hospital mortality or discharge to hospice; secondary outcomes included need for mechanical ventilation on day2+, and rates of non-POA-acute kidney injury (AKI). Results At 221 hospitals between March–December 2020, 39,517 (74%) of 53,431 stable COVID-19 admits received IEAT. Patient and encounter characteristics are shown in Table 1. The crude mortality rates were 12.2% in IEAT recipients and 10.9% in controls. In adjusted analysis of patients who survived beyond admission day, mortality was 11.57% (95% CI 11.24-11.90%) in IEAT recipients and 11.23% (95% CI 10.72-11.74) in controls, for a difference of 0.34% (95% CI -0.23-0.91%, p = 0.24). Subsequent mechanical ventilation occurred similarly between groups (5.72% vs. 5.77%, p=0.83). The adjusted rate of AKI was 2.47% (95% CI 2.31-2.64%) in IEAT recipients, and 3.04% (95% CI 2.74-3.35%) in controls, for a difference of -0.57% (95% CI -0.92-0.22%, p = 0.0014). Table 1.Demographics, clinical and hospital characteristics for patients treated with initial empiric antibiotic therapy (intervention) versus those not treated (control).Figure 1.Standardized mean differences in included covariates before and after several matching strategies comparing covariate values for patients treated and not treated empirically with antibiotics Conclusion In patients with COVID19 initially admitted to the ward, IEAT was not associated with a reduction in mortality or deterioration requiring mechanical ventilation, but with a clinically insignificant reduction in AKI. Empiric antibiotics can likely be safely withheld in this population. Disclosures All Authors: No reported disclosures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.382
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
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